R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("elasticnet-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('elasticnet')
Loading required package: lars
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "cv.enet"
> 
> ### * cv.enet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cv.enet
> ### Title: Computes K-fold cross-validated error curve for elastic net
> ### Aliases: cv.enet
> ### Keywords: regression
> 
> ### ** Examples
> 
> data(diabetes)
> attach(diabetes)
> ## use the L1 fraction norm as the tuning parameter
> cv.enet(x2,y,lambda=0.05,s=seq(0,1,length=100),mode="fraction",trace=TRUE,max.steps=80)

 CV Fold 1 


 CV Fold 2 


 CV Fold 3 


 CV Fold 4 


 CV Fold 5 


 CV Fold 6 


 CV Fold 7 


 CV Fold 8 


 CV Fold 9 


 CV Fold 10 

> ## use the number of steps as the tuning parameter
> cv.enet(x2,y,lambda=0.05,s=1:50,mode="step")
> detach(diabetes)
> 
> 
> 
> cleanEx(); ..nameEx <- "enet"
> 
> ### * enet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: enet
> ### Title: Fits Elastic Net regression models
> ### Aliases: enet
> ### Keywords: regression
> 
> ### ** Examples
> 
> data(diabetes)
> attach(diabetes)
> ##fit the lasso model (treated as a special case of the elastic net)
> object1 <- enet(x,y,lambda=0)
> plot(object1)
> ##fit the elastic net model with lambda=1.
> object2 <- enet(x,y,lambda=1) 
> plot(object2)
> ##early stopping after 50 LARS-EN steps
> object4 <- enet(x2,y,lambda=0.5,max.steps=50)
> plot(object4)
> detach(diabetes)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.enet"
> 
> ### * plot.enet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.enet
> ### Title: Plot method for enet objects
> ### Aliases: plot.enet
> ### Keywords: hplot methods
> 
> ### ** Examples
> 
> data(diabetes)
> attach(diabetes)
> object <- enet(x,y,lambda=1)
> par(mfrow=c(2,2))
> plot(object)
> plot(object,xvar="step")
> detach(diabetes)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "predict.enet"
> 
> ### * predict.enet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.enet
> ### Title: Make predictions or extract coefficients from a fitted elastic
> ###   net model
> ### Aliases: predict.enet
> ### Keywords: regression methods
> 
> ### ** Examples
> 
> data(diabetes)
> attach(diabetes)
> object <- enet(x,y,lambda=0.1)
> ### make predictions at the values in x, at each of the
> ### steps produced in object
> fits <- predict.enet(object, x, type="fit")
> ### extract the coefficient vector with L1 norm=2000
> coef2000 <- predict(object, s=2000, type="coef", mode="norm")
> ### extract the coefficient vector with L1 norm fraction=0.45
> coef.45 <- predict(object, s=0.45, type="coef", mode="fraction")
> detach(diabetes)
> 
> 
> 
> cleanEx(); ..nameEx <- "spca"
> 
> ### * spca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: spca
> ### Title: Sparse Principal Components Analysis
> ### Aliases: spca
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(pitprops)
> out1<-spca(pitprops,K=6,type="Gram",sparse="penalty",trace=TRUE,para=c(0.06,0.16,0.1,0.5,0.5,0.5))
iterations 10
iterations 20
iterations 30
iterations 40
iterations 50
iterations 60
> ## print the object out1
> out1

Call:
spca(x = pitprops, K = 6, para = c(0.06, 0.16, 0.1, 0.5, 0.5, 
    0.5), type = "Gram", sparse = "penalty", trace = TRUE)

6 sparse PCs 
Pct. of exp. var. : 28.0 14.0 13.3  7.4  6.8  6.2 
Num. of non-zero loadings :  7 4 4 1 1 1 
Sparse loadings 
           PC1    PC2    PC3 PC4 PC5 PC6
topdiam -0.477  0.000  0.000   0   0   0
length  -0.476  0.000  0.000   0   0   0
moist    0.000  0.785  0.000   0   0   0
testsg   0.000  0.619  0.000   0   0   0
ovensg   0.177  0.000  0.641   0   0   0
ringtop  0.000  0.000  0.589   0   0   0
ringbut -0.250  0.000  0.492   0   0   0
bowmax  -0.344 -0.021  0.000   0   0   0
bowdist -0.416  0.000  0.000   0   0   0
whorls  -0.400  0.000  0.000   0   0   0
clear    0.000  0.000  0.000  -1   0   0
knots    0.000  0.013  0.000   0  -1   0
diaknot  0.000  0.000 -0.016   0   0   1
> out2<-spca(pitprops,K=6,type="Gram",sparse="varnum",trace=TRUE,para=c(7,4,4,1,1,1))
iterations 10
iterations 20
iterations 30
iterations 40
> out2

Call:
spca(x = pitprops, K = 6, para = c(7, 4, 4, 1, 1, 1), type = "Gram", 
    sparse = "varnum", trace = TRUE)

6 sparse PCs 
Pct. of exp. var. : 28.2 13.9 13.1  7.4  6.8  6.3 
Num. of non-zero loadings :  7 4 4 1 1 1 
Sparse loadings 
           PC1    PC2    PC3 PC4 PC5 PC6
topdiam -0.477  0.003  0.000   0   0   0
length  -0.469  0.000  0.000   0   0   0
moist    0.000  0.785  0.000   0   0   0
testsg   0.000  0.619  0.000   0   0   0
ovensg   0.180  0.000  0.656   0   0   0
ringtop  0.000  0.000  0.589   0   0   0
ringbut -0.290  0.000  0.470   0   0   0
bowmax  -0.343 -0.029 -0.048   0   0   0
bowdist -0.414  0.000  0.000   0   0   0
whorls  -0.383  0.000  0.000   0   0   0
clear    0.000  0.000  0.000  -1   0   0
knots    0.000  0.000  0.000   0  -1   0
diaknot  0.000  0.000  0.000   0   0   1
> ## to see the contents of out2
> names(out2) 
[1] "call"     "type"     "K"        "loadings" "pev"      "var.all"  "vn"      
[8] "para"     "lambda"  
> ## to get the loadings
> out2$loadings
               PC1          PC2         PC3 PC4 PC5 PC6
topdiam -0.4774878  0.002735770  0.00000000   0   0   0
length  -0.4691409  0.000000000  0.00000000   0   0   0
moist    0.0000000  0.785205834  0.00000000   0   0   0
testsg   0.0000000  0.618547389  0.00000000   0   0   0
ovensg   0.1797963  0.000000000  0.65551907   0   0   0
ringtop  0.0000000  0.000000000  0.58924631   0   0   0
ringbut -0.2898492  0.000000000  0.46990985   0   0   0
bowmax  -0.3425338 -0.029042064 -0.04762634   0   0   0
bowdist -0.4138718  0.000000000  0.00000000   0   0   0
whorls  -0.3833453  0.000000000  0.00000000   0   0   0
clear    0.0000000  0.000000000  0.00000000  -1   0   0
knots    0.0000000  0.000000000  0.00000000   0  -1   0
diaknot  0.0000000  0.000000000  0.00000000   0   0   1
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  23.02 4.01 27.26 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')